Traffic congestion not only severely impacts residents’ daily travel quality and increases travel costs, but also triggers traffic accidents, causes environmental pollution, and leads to resource waste. There is a practical need to implement engineering measures simultaneously across multiple intersections to mitigate urban road traffic congestion, which necessitates in-depth research into selecting critical intersection clusters. Based on existing research, the relationship between vehicle emissions and the degree of saturation was derived. The network efficiency evaluation metric was refined using the degree of saturation, and a model linking vehicle emissions to network efficiency was established. A validation experiment was designed using the core road network of Xining City, Qinghai Province, as an example. The results indicate that vehicular exhaust emissions per kilometer are proportional to the saturation degree metric value. The network efficiency metric is inversely proportional to the network’s overall (or average) saturation degree. Vehicular exhaust emissions exhibit an inverse relationship with network efficiency. As the road traffic operational state shifts from congestion to free-flow conditions, for every 1-unit increase in network efficiency value, the average exhaust emissions per vehicle per kilometer decrease by 3.976 kg. Different congestion mitigation node selection schemes correspond to varying total emission reductions during the morning peak. When ranked by the magnitude of increase in network efficiency (from the largest increase to the smallest), the corresponding total morning peak emission reductions gradually decrease in a stepwise manner. According to the C260 and C360 experimental results, compared to the worst node cluster selection scheme, the optimal node cluster selection scheme can reduce vehicular exhaust emissions by 4441 kg and 6616 kg, respectively. These findings provide valuable theoretical and practical insights for implementing energy-saving and emission reduction strategies in urban traffic management.
Ma et al. (Mon,) studied this question.